Assessing the accuracy of template-based structure prediction metaservers by comparison with structural genomics structures

Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps

We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.

Cross-activating c-Met/β1 integrin complex drives metastasis and invasive resistance in cancer

The molecular underpinnings of invasion, a hallmark of cancer, have been defined in terms of individual mediators but crucial interactions between these mediators remain undefined. In xenograft models and patient specimens, we identified a c-Met/β1 integrin complex that formed during significant invasive oncologic processes: breast cancer metastases and glioblastoma invasive resistance to antiangiogenic VEGF neutralizing antibody, bevacizumab. Inducing c-Met/β1 complex formation through an engineered inducible heterodimerization system promoted features crucial to overcoming stressors during metastases or antiangiogenic therapy: migration in the primary site, survival under hypoxia, and extravasation out of circulation. c-Met/β1 complex formation was up-regulated by hypoxia, while VEGF binding VEGFR2 sequestered c-Met and β1 integrin, preventing their binding. Complex formation promoted ligand-independent receptor activation, with integrin-linked kinase phosphorylating c-Met and crystallography revealing the c-Met/β1 complex to maintain the high-affinity β1 integrin conformation. Site-directed mutagenesis verified the necessity for c-Met/β1 binding of amino acids predicted by crystallography to mediate their extracellular interaction. Far-Western blotting and sequential immunoprecipitation revealed that c-Met displaced α5 integrin from β1 integrin, creating a complex with much greater affinity for fibronectin (FN) than α5β1. Thus, tumor cells adapt to microenvironmental stressors induced by metastases or bevacizumab by coopting receptors, which normally promote both cell migration modes: chemotaxis, movement toward concentrations of environmental chemoattractants, and haptotaxis, movement controlled by the relative strengths of peripheral adhesions. Tumor cells then redirect these receptors away from their conventional binding partners, forming a powerful structural c-Met/β1 complex whose ligand-independent cross-activation and robust affinity for FN drive invasive oncologic processes.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

The value of protein structure classification information-Surveying the scientific literature

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from the resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function. Knowledge of the atomic details of protein structures is therefore a key to understanding their function. Advances in high throughput experimental technologies have lead to an exponential increase in the availability of known protein sequences. Although strong progress has been made in experimental protein structure determination, it remains a fact that more than 99% of structural information is provided by computational modeling methods. We describe here a novel structure prediction method, SmotifTF, which uses a unique library of known protein fragments to assemble the three-dimensional structure of a sequence. The fragment library has saturated over time and therefore provides a complete set of building blocks required for model building. The method performs competitively compared to existing methods of structure prediction.

Covering complete proteomes with X-ray structures: a current snapshot

Structural genomics programs have developed and applied structure-determination pipelines to a wide range of protein targets, facilitating the visualization of macromolecular interactions and the understanding of their molecular and biochemical functions. The fundamental question of whether three-dimensional structures of all proteins and all functional annotations can be determined using X-ray crystallography is investigated. A first-of-its-kind large-scale analysis of crystallization propensity for all proteins encoded in 1953 fully sequenced genomes was performed. It is shown that current X-ray crystallographic knowhow combined with homology modeling can provide structures for 25% of modeling families (protein clusters for which structural models can be obtained through homology modeling), with at least one structural model produced for each Gene Ontology functional annotation. The coverage varies between superkingdoms, with 19% for eukaryotes, 35% for bacteria and 49% for archaea, and with those of viruses following the coverage values of their hosts. It is shown that the crystallization propensities of proteomes from the taxonomic superkingdoms are distinct. The use of knowledge-based target selection is shown to substantially increase the ability to produce X-ray structures. It is demonstrated that the human proteome has one of the highest attainable coverage values among eukaryotes, and GPCR membrane proteins suitable for X-ray structure determination were determined.

Structural biology of TRP channels

Membrane proteins remain challenging targets for structural biologists, despite recent technical developments regarding sample preparation and structure determination. We review recent progress towards a structural understanding of TRP channels and the techniques used to that end. We discuss available low-resolution structures from electron microscopy (EM), X-ray crystallography, and nuclear magnetic resonance (NMR) and review the resulting insights into TRP channel function for various subfamily members. The recent high-resolution structure of TRPV1 is discussed in more detail in Chapter 11. We also consider the opportunities and challenges of using the accumulating structural information on TRPs and homologous proteins for deducing full-length structures of different TRP channel subfamilies, such as building homology models. Finally, we close by summarizing the outlook of the "holy grail" of understanding in atomic detail the diverse functions of TRP channels.

The quality and validation of structures from structural genomics

Quality control of three-dimensional structures of macromolecules is a critical step to ensure the integrity of structural biology data, especially those produced by structural genomics centers. Whereas the Protein Data Bank (PDB) has proven to be a remarkable success overall, the inconsistent quality of structures reveals a lack of universal standards for structure/deposit validation. Here, we review the state-of-the-art methods used in macromolecular structure validation, focusing on validation of structures determined by X-ray crystallography. We describe some general protocols used in the rebuilding and re-refinement of problematic structural models. We also briefly discuss some frontier areas of structure validation, including refinement of protein-ligand complexes, automation of structure redetermination, and the use of NMR structures and computational models to solve X-ray crystal structures by molecular replacement.